Risk Informed In-Service Inspection (RI-ISI) aims at prioritising the components for inspection within the permissible risk level thereby
avoiding unnecessary inspections. Various constraints such as risk level, radiation exposure to the workers and cost of inspections are
encountered, while planning the inspection programme. This problem has been attempted to solve using genetic algorithms, which has
already established its suitability in optimizing Surveillance and Maintenance activities in Nuclear Power Plants. The paper describes the
application of genetic algorithm in optimizing the ISI of feeders, which are large in number and also fall in the same inspection category.